Abstract 1607: Airway gene expression alterations associated with lung cancer chemoprevention using green tea extract
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer death in the United States and in the world. Identifying an effective chemopreventive agent for lung cancer would reduce lung cancer mortality by reversing, preventing, or delaying carcinogenic progression. Several randomized, controlled lung cancer chemoprevention trials have produced neutral or harmful results; with the exception of oral iloprost that demonstrated a small, but significant improvement in histology after 6 months of treatment in former smokers. One of the challenges in lung cancer chemoprevention trials have been the lack of surrogate endpoints to establish drug efficacy. In this study we use molecular profiling to study alterations assoicated with changes in airway histology and treatment with a promising chemopreventative, green tea extract. Airway epithelial cells were collected from patients with bronchial dysplasia during bronchoscopy at baseline, on-treatment, and post-treatment with green tea extract or placebo ranging from 2 to 6 months (n=27 patients, n=63 samples). RNA from the samples was processed and hybridized to Affymetrix Human Gene 1.0 ST arrays. Gene-level expression data was obtained using the Robust Multiarray Average (RMA) algorithm and ANOVA and linear modeling strategies were used to identify gene expression alterations associated with dysplasia regression and green tea extract treatment. Cancer and smoking-related pathway gene expression signatures were used to predict the pathway activation of each sample using a binary regression model. Gene set enrichment analysis (GSEA) was used to identify important biological pathways. Airway gene expression alterations associated with dysplasia regression were identified and pathways such as p53 and mTOR signaling were enriched among genes up-regulated in airways with dysplasia. The E2F3 oncogenic pathway was also found to be significantly altered in airways with dysplasia (p<0.05). The effect of green tea extract on airway gene expression was more pronounced among former versus current smokers. As a result, the degree to which pathways were modulated by green tea extract varied with smoking status. Pathways related to metabolism of xenobiotics, and glutathione, and retinol were decreased among treated current smokers while genes related to oxidative phosphorylation and DNA repair were increased in treated former smokers (FDR q-value<0.05). Our studies suggest that airway gene expression is altered in high-risk smokers with premalignant airway lesions and that this airway “field of injury” can be modulated by treatment with green tea extract especially among former smokers. We are currently investigating whether airway gene-expression can serve as an intermediate biomarker of response to green tea extract and identify those smokers who are most likely to benefit from this type of chemopreventive strategy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1607. doi:1538-7445.AM2012-1607
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".